A content device and method is disclosed to include a processing device to process streaming video content. A fingerprinter receives captured frames of the streaming video content and, for each frame of a plurality of the captured frames, generates a one-dimensional histogram function of pixel values and transforms the histogram function with a Fast Fourier Transform (FFT), to generate a plurality of complex values for the frame. The fingerprinter further, for each of the plurality of complex values, assigns a binary one (“1”) when a real part of the complex value is greater than zero (“0”) and assigns a binary zero (“0”) when the real part is less than or equal to zero, to generate a plurality of bits. The fingerprinter further concatenates a specific number of the bits to generate a fingerprint for the frame.

An apparatus for biometric security having a biometric scanner for capturing over a first field of view image data representative of one or more biometric objects associated with a subject, and a presentation attack detection system for capturing over a second field at one or more locations along the subject information indicative of presence of the one or more biometric objects. One or more processers utilizes the image data received from the biometric scanner to select such one or more locations, and to direct the second field of view of the presentation attack detection system to obtain the information along one or more of the selected one or more locations, and to determine in accordance with the information when the first field of view contains a true or fake presentation to the biometric scanner.

A system and method for identifying a location using image recognition. STR listing images are analyzed and assigned an archetype. Optionally, STR listing images are analyzed with an object detection model and associated with archetypes. The STR dwelling unit type may be determined from the combination of STR image archetypes. A location for the STR listing may then be determined by comparing to images of dwelling units retrieved from databases.

Technologies for guiding an agricultural vehicle through crop rows using a camera and signal processing to locate the crop row or centers of the crop row. The signal processing uses a filter to filter data from images captured by the camera and locates the row or the centers based on the filtered data. The filter is generated based on a signal processing transform and an initial image of the crop row captured by the camera. The filter is applied to subsequent images of the crop row captured by the camera. In some embodiments, the camera includes one lens. For example, monocular computer vision is used in some embodiments. Also, in some embodiments, a central processing unit generates the filter based on the transform and the initial image of the crop row and applies the generated filter to the subsequent images of the row.

Disclosed is an AI acoustic camera including an acoustic source localizing means unit of generating position-specific acoustic level data by determining a position of an acoustic source, an AI acoustic analysis unit of recognizing a type of acoustic source estimated as an abnormal acoustic source by extracting a regeneration time domain acoustic signal for the acoustic source with the determined position and AI-learning and recognizing an acoustic feature image of the extracted time domain acoustic signal, an object recognition unit of recognizing a type of object positioned in the acoustic source through image analysis of an area recognized as that the acoustic source is positioned, and a determination unit of determining the acoustic source as a true acoustic source when the type of acoustic source and the type of object have commonality.

Fast and scalable architectures and methods adaptable to available resources, that (1) compute 2-D convolutions using 1-D convolutions, (2) provide fast transposition and accumulation of results for computing fast cross-correlations or 2-D convolutions, and (3) provide parallel computations using pipelined 1-D convolvers. Additionally, fast and scalable architectures and methods that compute 2-D linear convolutions using Discrete Periodic Radon Transforms (DPRTs) including the use of scalable DPRT, Fast DPRT, and fast 1-D convolutions.

A method and electronic device for radar-based face authentication anti-spoofing for determining access to the electronic device. The electronic device includes a radar transceiver and at least one processor. The at least one processor is configured to transmit, via the transceiver, a first set of signals, generate a channel impulse response (CIR) based on receipt of reflections of the first set of signals, detect a first CIR tap in the CIR, determine a selection of CIR data based on the detected first CIR tap, determine a profile matching metric based on comparison of the selection of CIR data to a set of predetermined reference signals, and determine whether to allow access to the electronic device based on comparison of the profile matching metric to a profile matching threshold.

This disclosure is directed to an image authenticity detection method and apparatus. The method includes: obtaining an image; removing low-frequency information from the image to obtain first image information of the image; denoising the first image information to obtain second image information; determining, based on a difference between the first image information and the second image information, a fixed pattern noise feature map corresponding to the image; analyzing distribution of fixed pattern noise in the fixed pattern noise feature map, the fixed pattern noise being inherent noise from a camera sensor and not interfered by image content; and detecting, based on the distribution, authenticity of the image to obtain an authenticity detection result of the image.

A method for extracting a fingerprint of a video having a plurality of frames includes obtaining a plurality of pixel value matrices from each of the plurality of frames, calculating maximum values of average pixel values in each axis of the plurality of pixel value matrices for each of the plurality of frames, and calculating the fingerprint of the video based on a temporal correlation of the maximum values calculated for the plurality of frames.

A method for extracting a fingerprint of a video includes calculating 2D discrete cosine transform (DCT) coefficients from each of the plurality of frames of the video, extracting, from the 2D DCT coefficients, a coefficient having a basis satisfying at least one of up-down symmetry or left-right symmetry, and calculating a fingerprint of the video based on the extracted coefficient.